To validate the effectiveness of proposed FMP, we also show the prediction performance and fairness metric trade-off compared with fairness-boosting methods, including adversarial debiasing [1]} and adding regularization [2]}. Similar to [3]}, the output of GNNs is the input of adversary and the goal of adversary is to predict the node sensitive attribute. We also adopt several backbones for these two methods, including MLP, GCN, GAT and SGC. We randomly split \(50\%/25\%/25\%\)  for training, validation and test dataset. Figure REF  shows the pareto optimality curve for all methods, where right-bottom corner point represents the ideal performance (highest accuracy and lowest prediction bias). From the results, we list the following observations as follows:
